Semi-automated and inter-active system and method for analyzing patent landscapes

ABSTRACT

A semi-automated method for interactively analyzing a patent landscape in one embodiment includes retrieving a plurality of relevant patents indicative of a predetermined conceptual region of the patent landscape from a patent repository using a query. Competitive analysis of the plurality of relevant patents is conducted using an interactive network-based visualization technique. The competitive analysis is used for intellectual property enforcement, due diligence, and strategic investment analysis.

BACKGROUND

Modem business intelligence routinely makes extensive use of customerand transactional data obtained from databases stored in datawarehouses. Such business intelligence may typically be obtained byposing an analytical search and/or query to one or more associatedrelational databases. Intellectual property (IP) intelligence, inparticular, is very useful to the competitive advantage of a businessentity. The business entity may seek to maximize the value of its IP byinvestigating and identifying areas of relevant patents for example,“white space” in an industry, where white space is a term generally usedto designate one or more technical fields in which little or no IP mayexist therefore helping to identify opportunities.

A patent landscape study is a comprehensive analysis of patents andpatent applications to benefit business managers, product managers,technical personnel, and patent attorneys. A patent landscape studyprovides a map with detailed patent activity in specific technologyareas that significantly improves the ability to make sound majorbusiness decisions. Each patent landscape study may include acomprehensive report and a customized database. The report may includetrends and directions in the technology field of interest and includespivotal information on key technology providers. The database mayinclude essential data on each patent, a description of its importance,an assessment of each invention as either a fundamental discovery or anincremental improvement, and the technical problems it solves. Intypical cases, the patents are ranked by relevance to the technology athand or to the demands of the customer.

However, most of the known analysis tools are inadequate, cumbersome,labor intensive, and cannot perform automated analysis. Also, the knowntools cannot perform higher-order analysis. In other words, the toolsperform only superficial analysis and require knowledgeable and skilledreview and analysis to generate a useful output. Since there is asubjective nature to the analysis, the output can vary depending uponthe person performing the searching and refinement of the data.Furthermore, most tools operate in batch mode, so the process itselfcannot provide additional information, since it is not transparent tothe user.

There is a general need for improved competitive analytics forIntellectual Property. An automated, interactive system and method thatis easy to use and can perform a higher-order analysis of patents isalso desirable.

BRIEF DESCRIPTION

In accordance with one exemplary embodiment, a semi-automated method forinteractively analyzing a patent landscape is disclosed. The methodincludes retrieving a plurality of relevant patents indicative of apredetermined conceptual region of the patent landscape from a patentrepository using a query. Competitive analysis of the plurality ofrelevant patents using an interactive network-based visualizationtechnique is conducted. The competitive analysis may be used forintellectual property enforcement, due diligence, and strategicinvestment analysis.

In accordance with another exemplary embodiment, a semi-automated methodfor interactively analyzing a patent landscape is disclosed. The methodincludes retrieving a plurality of relevant patents indicative of apredetermined conceptual region of the patent landscape from a patentrepository using a query. Competitive analysis of the plurality ofrelevant patents using an interactive network-based visualizationtechnique is conducted. The interactive network-based visualizationtechnique is augmented using one or more reasoning techniques. Thecompetitive analysis may be used for shaping the development of newinventions (avoiding known barriers to practice while pursuing whitespaces), deciding on each invention's potential for a successful patentfiling or on its merits for a renewal, enforcing intellectualproperties, performing due diligences, identifying suitable jointventure partners, and defining strategic investments.

In accordance with another exemplary embodiment, a semi-automated methodfor interactively analyzing a patent landscape is disclosed. The methodincludes retrieving a plurality of relevant patents indicative of apredetermined conceptual region of the patent landscape from a patentrepository using a query. Competitive analysis of the plurality ofrelevant patents using an interactive network-based visualizationtechnique is conducted. The interactive network-based visualizationtechnique is augmented using one or more reasoning techniques. The whitespace opportunities are evaluated based on the augmented interactivenetwork-based visualization technique. The competitive analysis may beused for shaping the development of new inventions, deciding on eachinvention's potential for a successful patent filing or on its meritsfor a renewal, enforcing intellectual properties, performing duediligences, identifying suitable joint venture partners, and definingstrategic investments.

In accordance with another exemplary embodiment, a semi-automated systemfor interactively analyzing a patent landscape is disclosed. The systemincludes an input device configured to input a query to a patentdatabase. An interactive analytical tool is communicatively coupled tothe patent data base and configured to extract a plurality of relevantpatents indicative of a predetermined conceptual region of the patentlandscape; conduct competitive analysis of the plurality of relevantpatents using an interactive network-based visualization technique;augment the interactive network-based visualization technique using oneor more reasoning techniques; and evaluate white space opportunitiesbased on the augmented interactive network-based visualizationtechnique. An output device is configured to display an outputindicative of an analysis output from the analytical tool. The outputmay be used for shaping the development of new inventions, deciding oneach invention's potential for a successful patent filing or on itsmerits for a renewal, enforcing intellectual properties, performing duediligences, identifying suitable joint venture partners, and definingstrategic investments.

DRAWINGS

These and other features, aspects, and advantages of the presentinvention will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 is a diagrammatical representation of a semi-automated system forinteractively analyzing a patent landscape in accordance with anexemplary embodiment;

FIG. 2 is a flowchart for the processing in accordance with an exemplaryembodiment,

FIG. 3 is a patent map generated by an analytical tool of asemi-automated system configured for interactively analyzing a patentlandscape in accordance with an exemplary embodiment;

FIG. 4 is a patent map generated by an analytical tool of asemi-automated system configured for interactively analyzing a patentlandscape in accordance with an exemplary embodiment;

FIG. 5 is a focused view of a patent map generated by an analytical toolof a semi-automated system configured for interactively analyzing apatent landscape in accordance with an exemplary embodiment;

FIG. 6 is a an interactive graphical representation generated by theanalytical tool using temporal reasoning in accordance with an exemplaryembodiment;

FIG. 7 is a graphical representation constructed based on a single nodeperspective in accordance with an exemplary embodiment; and

FIG. 8 is process flow chart for landscape analysis using asemi-automated system in accordance with an exemplary embodiment.

DETAILED DESCRIPTION

The system and techniques relate generally to the analysis of patents,and more specifically, to a semi-automated and interactive system andmethod for analyzing patent landscape and suggesting potentialconclusions and predictions.

In accordance with the embodiments, a semi-automated system and methodfor interactively analyzing a patent landscape is disclosed. Oneexemplary technique includes performing competitive analysis byanalyzing a database with potentially relevant patents indicative of apredetermined conceptual region of the patent landscape, understandingcross-company patent landscape, understanding relative strengths andweaknesses, understanding semantically adjacent domains, understandingtemporal trends, and identifying seminal patents categorized by area.The technique may further include intellectual property prospecting byidentifying white space opportunities, and projecting temporal trends byidentifying emerging opportunities and estimating growth potentials.Automated and/or semi-automated interactive reasoning and visualizationfacilitates in the performance of strategic assessments, comparativepatent analyses, assessing the value of white spaces, and to projectpotential growth of emerging opportunities.

Referring to FIG. 1, a semi-automated system 10, for example a computersystem for interactively analyzing a patent landscape is disclosed. Thesystem 10 includes a data warehouse 12 that may include, in particular,one or more databases useful in intellectual property analysis such as:a worldwide patent (WWP) database; a web, scientific, and news (WSN)database; a financial (EFD) database, or the like. The data warehouse 12may also include information about the documents included in the variousdatabase included therein. The database 12 can be, for example, one ormore local databases coupled to a computing device 14 or one or moredatabases accessible via an internet, intranet or area network. Thecomputing device 14 hosting an analytics tool 15 may access the datawarehouse 12 to perform a number of functions, including: extractingpatents and related documents, automatically classifying patents,performing contingency analysis, and analyzing various relationshipsamong patents and companies, as described in greater detail herein.

An analytical search/query 16 is coupled to the analytics tool 15 andultimately to the data warehouse 12 (repository) via an input device 17,for example a keyboard, mouse, touchscreen, or other input mechanismsuch as a microphone with speech to text conversion. One or more termssuch as keywords, classification codes or other characteristics are usedto perform searching of the database(s).

In one example, a user interested in patent analysis, for example, apatent landscape analysis, would initiate queries via the input device17 for processing for a task, herein broadly denoted as a landscapeoutput 18. The searching in this context may reference the term patents,and in this context it refers to patents and patent applications thatcan be from the various countries, regional offices, and filingorganizations such as the World Intellectual Property Organization(WIPO) that administers the Patent Cooperation Treaty. The output 18 maybe displayed via a display device 19 such as a monitor. A set of domainknowledge inputs 20 provided by one or more domain experts or users maybe applied to execute or enhance one or more of the functions performedby the analytics tool 15. For example, a process of analyzingrelationships among patents and companies may invoke both the expertiseof an individual skilled in the technology of document classificationand the expertise of a domain expert skilled in the technology of thepatents under analysis.

In one example, the analytics tool 15 may use a search tool to identifya set of companies in an industry of interest; retrieve patents andother related materials, including web pages that describe technologyand products currently relevant to the industry of interest, or thelike. For example, the search is executed, and a query is performedusing the results of the search to retrieve a collection of documentsmatching initial search criteria. For example, an industry may beselected, given one or more companies that are representative of thatindustry. One or more terms such as keywords, industry classificationcodes or other characteristics that describe the selected industry.Patents and other files, either assigned to the selected companies orrelated to the keywords, may be extracted from the database to form afirst set, or collection of extracted documents. Second tier companies(other companies related to the given companies but not represented inthe first set) may be found by looking across structured features andunstructured features for common characteristics shared by the patentsand the other files in the first document set. Examples of structuredfeatures in a patent may include: name of inventor, name of assignee,classification of the patent, and documents referenced by the patent.Examples of unstructured features may include regular text, such as maybe found in the abstract, the claim language, or in the title of thepatent or document. An unstructured feature that may be converted into astructured feature is referred to herein as an “annotation.”

This conversion process may include, for example, recognizing a pattern,using a synonym from a dictionary, or equating the idea conveyed by textto a structured concept. Patents and other files assigned to the secondtier companies may be extracted from the database warehouse 12 to form asecond set of documents. Additional documents related to the second tiercompanies may be retrieved using the keywords and/or one or more webqueries on an existing web store, and may be included in the seconddocument set. The first document set and the second document set may becombined to form yet another set, referred to herein as a third documentset. Subsequently, the analytics tool 15 may use a documentclassification technology, or taxonomy generation technology, toclassify the retrieved patents and other related materials. It should benoted herein that the system 10 described herein is an exemplaryembodiment and should not be construed in any way as limiting. Theconfiguration of the system 10 may vary depending upon the application.

In accordance with an exemplary embodiment of the present system, theanalytics tool 15 may use statistical improbable phrases (SIP) to defineunique signatures for each patent. SIP is also used more generally torefer to a search string likely to generate meaningful results from asearch engine; that is, a string whose chance of occurring in adesirable result is much greater than its chance of occurring in anon-desirable result. Metric parameters for SIP include term frequency(denoted tf_(ij)) by of a term “w_(i)” in a document d_(j) is the numberof occurrences of “w_(i)” in “d_(j)”, collection frequency (denoted bycf_(i)) of a term “w_(i)” is the total number of occurrences of a term“w_(i)” in a collection of documents, document frequency (denoted bydf_(i)) of a term “w_(i)” is the number of documents in the collectionin which “w_(i)” occurs at least once.

More specifically, the term frequency tf_(ij) is computed as:

${{t\; f_{i,j}} = {\sum\limits_{r = 1}^{L_{j}}{I_{1}( {i,{r;j}} )}}},{{{where}\mspace{14mu} {I_{1}( {i,{r;j}} )}} = \{ \begin{matrix}1 & {{{if}\mspace{14mu} w_{i}} = {w_{r}\mspace{14mu} {in}\mspace{14mu} d_{j}}} \\0 & {otherwise}\end{matrix} }$

where I₁ is a binary indicator signaling the presence or absence of word“w_(i)” in document “d_(j)”; the collection frequency cf_(i) is computedas: cf_(i)=Σ_(j=1) ^(D)tf_(i,j); the document frequency df_(i) iscomputed as:

${{d\; f_{i}} = {\sum\limits_{j = 1}^{D}{I_{2}( {i,j} )}}},{{{where}\mspace{14mu} {I_{2}( {i,j} )}} = \{ \begin{matrix}{1,} & {{{if}\mspace{14mu} t_{i,j}} > 0} \\{0,} & {otherwise}\end{matrix} }$

where I₂ is a binary indicator signaling that word “w_(i)” occurred atleast once in document “d_(j)”. With these measures we can compute theinverse document frequency (denoted by idf_(i)), a metric that isminimized for those terms that occur in all documents and is maximizedfor terms that occur in a single document. Specifically the inversedocument frequency is computed as:

${i\; d\; f_{i}} = {{I\; D\; {F( w_{i} )}} = {\log ( \frac{D}{d\; f_{i}} )}}$

where “D” is the total number of documents in the collection D. A wholefamily of weighting schemes (denoted by TF.IDF) that combine a the termfrequency with an the inverse document frequency is computed as:

${T\; {F \cdot I}\; D\; {F( w_{i,j} )}} = {{t_{i,j} \times \log \frac{D}{d\; f_{i}}} = {t_{i,j} \times i\; d\; f_{i}}}$

An alternative weighting scheme is the residual inverse documentfrequency (denoted by RIDF), which is computed as:

${R\; I\; D\; {F( w_{i} )}} = {{{\log_{2}\frac{D}{d\; f_{i}}} - {\log_{2}( {1 - ^{\frac{- {cf}_{i}}{D}}} )}} = {{i\; d\; f_{i}} - {\log_{2}( {1 - ^{\frac{- {cf}_{i}}{D}}} )}}}$

“RIDF” is the difference between the logs of the “actual sample idf” andthe “idf expected”. “RIDF” tends to highlight technical terminology,names, and good keywords for information retrieval.

A process flow indicated by 21 according to one embodiment is depictedin FIG. 2. Although there are a number of depicted process steps, at anystep in this process, it is possible to export materials such as patentsof interest for further refinements or analyses. The processingcommences with identifying the relevant set of data for processingindicated by 22. In one embodiment, this is performed by establishingthe queries to define a set of relevant data from a larger data set thatmay be, for example, a collection of patents residing on one or moreservers and one or more databases. The queries can be vetted to obtain amore appropriate data set for the specific application via multiplerevisions to obtain the relevant data set.

The relevant set of data in one example is used to generate variouspresentations indicated by 24, which in one embodiment is performed byvisualizing the data as a concept view, patent view, and/or generatingvarious pie charts and heat maps such as by assignee, for example. Inone example the visualized data is represented as nodes with linksbetween the nodes wherein the size/shape of the nodes and thelength/thickness of the links can be used to indicate certaincharacteristics of the relationships. The set of relevant data can beexamined as to whether further processing is needed to augment the datapresentation indicated by 26. For example, a graph size may beaugmented, by adding new nodes and links. The user-provided node set maybe augmented with additional nodes mined from a corpus. It is alsopossible to complement the lexical description with semantic knowledge(domain ontologies and rule sets) and leverage relationships fromsimilar domains to increase graph connectivity (nodes and links).

If further processing of the set of relevant data is not needed toaugment the data presentation, then the data set can be examined as towhether further processing is required to establish a customizedperspective view depending on the requirements of a user indicated by28. If the customized perspective view is desired, then a requiredsubset of nodes and/or link of the visual presentation are selected fromamong the plurality of nodes and links to define the customizedperspective view indicated by 30. If the customized perspective view isnot desired, the data set can be examined as to whether competitiveassessment is needed indicated by 32. Then required subset of assigneeis selected from among the plurality of assignees for competitiveassessment indicated by 34. The strengths and weaknesses of the selectedassignees are analyzed indicated by 36. The competitive assessmentfacilitates the customization of the data presentation depending on theuser requirement.

If further processing of the set of relevant data to augment the datapresentation is needed, the set of relevant data can be examined toestablish if additional reasoning techniques need to be applied toaugment the data presentation indicated by 38. If it is established thatadditional reasoning techniques are required, then analogical, temporal,forecasting, inductive, deductive, taxonomical, similarity-basedreasoning techniques, or combinations thereof may be applied to augmentthe data presentation indicated by 40.

If it is established that additional reasoning technique is notrequired, a histogram of patent forward citations of the data set isgenerated indicated by 42. The process flow may further includeselecting a key required patent and visualizing the selected patent inpatent view along with similar patents of the data set indicated by 44.The process flow may also include generating a patent heat map of thedata set indicated by 46. It should be noted herein that the processflow illustrated herein is an exemplary embodiment and there is norequirement that the process flow proceed in a particular order, and theorder and number of the steps may vary depending on the requirement.

Referring to FIG. 3, a patent map 48 generated by the analytic tool isdisclosed for illustrative purposes. The map 48 includes a cluster ofretrieved references (cited patents) indicated by several nodes 50linked to each other via link lines 52. The map 48 is generated based onthe defined functions and domain, and in this example, a particularpatent is identified and the analytic tool applies certain filters tocreate the patent map 48. In the illustrated example, the map 48 isindicative of prognostic and health management area (PHM or conceptualregion). The functions or PHM 54 may be defined by keywords such asanomaly detection, anomaly identification, asset management,availability, control, or the like. The informatics 56 indicatesattributes such as bandwidth and communications. The domain 58 may bedefined by keywords including acoustic, deep sea, drilling, emissions,flow optimization, or the like. It should be noted herein that in themap 48, proximity of one node with one or more other nodes is indicativeof conceptual similarity in the patent landscape. The map 48 isexplained in greater detail with reference to subsequent figures.

Referring to FIG. 4, a concept perspective map view 60 is disclosed. Theconcept view 60 is generated by the analytical tool using, for example,an interactive network-based visualization technique for performingcompetitive analysis. In other words, the concept perspective map view60 is an interactive graphical representation of a plurality of nodesinter-coupled through links. Each node disclosed herein is indicative ofa number of patents of the patent landscape including a termrepresentative of the node. Each link is indicative of number of patentsof the patent landscape including one or more terms representative ofthe nodes coupled via the link. The map view 60 may be subjected tointeractive graphical representation via rotations and zoomingoperations.

In the illustrated example, the concept map view 60 is indicative ofcross-company landscape of two competitors, for example competitor ABC,and competitor XYZ in a predefined technology area. A few of the nodesand links are referenced herein for describing the illustratedembodiment. For example, one node 62 may be indicative of a sensordomain, another node 64 may be indicative of a monitoring domain,another node 66 may be indicative of an inverter domain, node 68 may beindicative of an optimal domain, node 70 may be indicative of apredictive domain, node 72 may be indicative of a fuel domain, and soforth. As noted, the node 62 is linked to node 64, via a link 74, inthis example.

A node diameter is indicative of the number of patents with reference tothe associated domain, international patent classification, UnitedStates patent classification, and so forth. The node color may also beindicative of a particular characteristic associated with a particularcompetitor. For example, a red node may be indicative of competitor ABChaving higher number of patents than the competitor XYZ in theassociated domain area. A green node may be indicative of competitor XYZhaving higher number of patents than the competitor ABC in theassociated domain area. The distance between two particular nodes may beindicative of similarity in domains associated with the particularnodes.

Similarly, the link color may also be indicative of a particularcharacteristic associated with a particular competitor. For example, ared link may be indicative of competitor ABC having higher number ofpatents than the competitor XYZ in the intersection of the domain areasassociated with the nodes coupled by the particular red link. In otherwords, if the node associated with sensor domain is linked via a redlink to the node associated with the monitoring domain, the red link maybe indicative of competitor ABC having higher number of patents than thecompetitor XYZ in the intersection of the domain areas (overlappingsensor and monitoring domain areas). A green link may be indicative ofcompetitor XYZ having higher number of patents than the competitor ABCin the intersection of the domain areas associated with the nodescoupled by the particular green link. Thickness of a particular link maybe indicative of the number of patents associated with the domainsindicative of the nodes coupled via the particular link. The disclosedexample is an exemplary embodiment and should not be construed aslimiting. The number of nodes, node color, link color, and domain areamay vary depending upon the application.

The visualization modes of the map 60 may be switched to provide furtherfeatures of the system. The visualization modes may include patentscope, patent status, assignee, node cardinality (patent countassociated to the node), link cardinality (patent count associated tothe link), filed date, issue date, patent quality, or combinationsthereof. The patent scope may include such items as title, abstract,claim(s), or entire specification. The patent status may includecurrently active issued patents, or issued patents, or issued andpublished patents as well as data related to active or abandonment. Theassignee scope may include reference assignee, or set of assigneesincluding reference assignee, post assignments, or all assignees. Thepatent quality may include seminal patents or all patents.

The concept map 60 provides a summary snapshot of the relationshipbetween the various patents and patent applications in a format that isrich in content to the viewer. Furthermore, the viewer can adjust theparameters on the fly and obtain various perspective views of therelationships.

Referring to FIG. 5, a perspective patent view of the entities andrelationships is represented as 76. As noted, the term patent as usedherein refers to issued patents and published patent applications.Issued patents and published patent applications may include UnitedStates issued patents and published patent applications, and foreignissued patents and published patent applications, reissued patents, orthe like. The patent map view 76 is generated by the analytical toolusing, for example, an interactive network-based visualization techniquefor performing competitive analysis. In other words, the patent map view76 is an interactive graphical representation of a plurality of nodesinter-coupled through links for a particular patent. For example, aplurality of nodes 78, 80, 82, 84, 86, 88, 90 and 92 are referencedherein, wherein each node denotes a patent. The nodes 80, 84, and 86 arecoupled to the node 78 via the links 94, 96, and 98 respectively. In theillustrated embodiment, the base patent 78 is located at the origin. Thesize of a node in this example is proportional to the number of forwardcitations, which refers to the number of times a patent is referenced byanother patent, typically via the Information Disclosure Statement inthe U.S. Patent Office although various search reports also trackrelated cases. A node diameter of a particular node is indicative of thenumber of patent forward citations with reference to the particularnode. The node color of a particular node may be indicative of aparticular assignee. Distance between two nodes may be indicative ofsimilarity in patents associated with the two particular nodes. A linkbetween two nodes may be indicative of a common factor associated withthe patents indicative of the nodes. A link indicated by a solid linemay be indicative of common inventor. A link indicated by a dashed linecan be used to indicate examination by a particular patent examiner.

It should be noted herein that relative strengths and weaknesses of oneor more assignees (competitors) can be analyzed based on a general view,competitive view, focused view, focused competitive view, orcombinations thereof of nodes and links. Factors including node size,and link thickness may be indicative of the general view of allassignees or a single assignee. The competitive view for a set ofassignees may include selecting reference assignee from a set ofassignees, node and link color representative of assignee with largerpatent count for selected terms (concepts), and changing referenceassignee within a set of assignees to analyze local intellectualproperty dominance. The focused view of the map includes focusing on anode of interest to sharpen analysis perspective and define a relativelysmaller intellectual property region.

In one embodiment, the focused view also includes establishing aconceptual node neighborhood. In other words, the conceptual nodeneighborhood includes viewing all other nodes directly linked to thenode-of-interest and identifying the closest conceptual neighbors. Otherintra-node links are not shown except for links coupled to thenode-of-interest. In the illustrated embodiment, the node-of-interest isthe node 78 and the conceptual node neighborhood includes nodes 80, 82,84, 86, 88, 90 and 92. The focused view further includes establishing aconceptual sub-graph neighborhood. In other words, the conceptualsub-graph neighborhood includes viewing all other nodes directly linkedto node-of-interest including all other intra-node links. The coverageof a set of keywords may also be evaluated by adding patent count ofdisjointed node intersections with a reference node-of-interest and thencomparing the added patent count to the patent count of the referencenode-of-interest to identify missing relevant keywords (semanticallyadjacent concepts).

In an alternate embodiment, a focused competitive view may be performedby evaluating coverage of a set of assignees. In other words, for eachnode-of-interest, the union of patent counts of all members of a set ofassignees is compared with total patent count to identify missingrelevant players in the conceptual region.

In certain other embodiments, maps may be generated to studysemantically adjacent domains. The subset of keywords may be modified toexplore semantically adjacent domains in the patent landscape. Specificnodes-of-interest may be selected to understand the adjacent domains. Inone illustrated embodiment, the adjacent domain may include prognostichealth management for oil and gas. In another embodiment, the adjacentdomain may include prognostic health management for aviation. Any numberof adjacent domains is envisaged depending on the application.

In the embodiments discussed above, the interactive graphicalrepresentation of a plurality of nodes inter-coupled through links maybe augmented using one or more reasoning techniques comprising lexicalreasoning, taxonomical reasoning, deductive reasoning, analogicalreasoning, temporal reasoning, or combinations thereof. Lexicalreasoning may include obtaining a set of keywords by automated keywordextraction and merging results. In other words, user-generated keywordsmay be merged with relevant words harvested by text-mining corpus. Thenan augmented graph may be created. The lexical reasoning also includesclassification codes in lieu of keywords wherein patents are classifiedusing US Classifications and International Patent Classifications tosegment patents into related groupings. The classificationcategorization is typically a lexical reasoning that is performed at thepatent offices and is initially based on some keyword analysis.

Taxonomical reasoning may include augmenting lexical information byusing domain ontologies. The number of links (connectivity) may beincreased by “inverse” inheritance. Ontology links are used to extendconnectivity of child node to parent node. For example, if node “B” is achild of node “A”, and node “B” is coupled to node “C”, then node “A” isalso coupled to node “C”. Taxonomical reasoning may include objecttaxonomy and function taxonomy. In an object taxonomy scenario, forexample, if node 1 is coupled to node 2, this may imply that node 2 is apart of node 1. For a function taxonomy scenario, for example, if node 3is coupled to node 4, this may imply that node 3 enables node 4. Thepotential use allows the user to represent taxonomical/ontologicaldomain knowledge to complement the lexical knowledge derived from thekeyword set. The connectivity in the graph (new links) is increased,thereby enabling identification of more patents for potentialintellectual property leverage, coverage, and region protection.

Deductive reasoning allows augmenting lexical information by usingdomain rules. A deductive rule of the kind “If A and B then C” impliesthat a more specific term “C” can be established if terms “A” and “B”are present. This would mean that if the intersection of “A” and “B”would entail C (the size of C is bounded by the size of theintersection). Such rules may be chained. It may be noted herein thatdeductive reasoning can add new, more specific nodes in the graph bycreating new specialized terms that might be used to cover morespecifically a niche market.

Analogy is the use of relationships established in one domain andtransferred to another domain. For example, if a user analyzes thelandscape of a particular domain and determines that node A is notcoupled to node D, this may imply that the intersection between node Aand node D is empty and is a potential white space opportunity. Yet, inanother particular domain, if the same node A is coupled to node D, itmay imply that there are patents that share both terms associated withnodes A and D. A user is able to extrapolate that relationship(intersection) to extend the patents of the link AD from one domain tothe other domain.

Referring to FIG. 6, a simplified interactive graphical representation100 generated by the analytical tool using temporal reasoning isdisclosed. The representation 100 includes a node 102 coupled to theneighboring nodes 104, 106, 108, 110, 112, 114 via associated links 116,118, 120, 122, 124, and 126. The representation 100 can be analyzed toexplore the temporal trends to visualize areas of growth.

The trend analysis includes analyzing size and thickness of nodes andlinks. New nodes are indicative of emerging concepts and new links maybe indicative of relationships between concepts. Temporal evolution canbe analyzed based on patent filing date or patent issue date and/orpatent priority date. The illustrated embodiment is indicative ofevolution of the concept associated with node 102 with reference toneighboring nodes 104, 106, 108, 110, 112, and 114 over a seventeen yearperiod extending from 1990 to 2007. It may be determined that as thesize of node 102 increased, node 108 experienced faster growth, node 110emerged in the year 2000 and grew, and nodes 112, 114 are the currentemerging areas. The potential use of temporal reasoning includesunderstanding and quantifying temporal dynamics in the region ofinterest, and identifying fast growing concepts, and emergingconnections.

As previously noted, the distance between the nodes is indicative ofsome attributes associated with the respective nodes and a correspondingsimilarity. For example, the similarity in the domains can be used tomake the distance relationship.

In certain embodiments, seminal patents categorized by area may beidentified. In one embodiment, highly coupled nodes in the graphicalrepresentation discussed above, may be indicative of seminal patents.The seminal patents may be optionally excluded based on the relative ageof the identified patents.

Referring to FIG. 7, a graphical representation 128 constructed based ona single node perspective is disclosed. In the illustrated embodiment,the representation 128 is based on a single node perspective, forexample node A represented by reference numeral 130. The representation128 further includes plurality of other nodes B, C, D, E, F representedby reference numerals 130, 132, 136, 138, and 140 respectively. The nodecardinality (patent count) of nodes 130, 132, 134, 136, 138, and 140 are100, 25, 25, 120, 75, and 10 respectively. It should be noted hereinthat the node cardinality mentioned herein are exemplary values andshould not be construed herein as limiting. The present links includeAB, AC, AD, AE, AF, BE, BF, CD, CF, and EF are represented by thereference numerals 142, 144, 146, 148, 150, 152, 154, 156, 158, and 160respectively. The missing links include BC, BD, CE, DE, DF representedby reference numerals 162, 164, 166, 168, and 170 respectively. Thepotential white spaces (missing links) are identified by identifyingpairs of nodes that are not directly coupled (there is no intersectionbetween the two terms) but that are coupled via a third, common node.These opportunities are listed and ranked according to the potentials ofthe nodes to be coupled.

In the illustrated embodiment, the missing links for the single node 130are identified by locating pairs of nodes that are not directly coupledbut are coupled (triangulation) via the node 130. The missing links areranked based on potential for extension of existing intellectualproperty (IP) space, for example combined node cardinality. The missinglinks are ranked by combined cardinality as follows:

DE→DA+AE=(120+75)=195

BD→BA+AD=(25+120)=145

DF→AD+DF=(120+10)=130

CE→CA+AE=(25+75)=100

BC→BA+AC=(25+25)=50

Although combined node cardinality is discussed, other prioritizationcriteria including market intelligence or information on intersectionstrength may be used. Temporal trends may be projected to identifyemerging opportunities and estimate their growth potentials. In theillustrated embodiment, neighboring nodes (conceptual peers) of node 130may be identified to compute peer temporal trends.

In certain embodiments, the prescience of patent (POP) score may becomputed in the region of white space opportunities for IP prospectingand forecasting.

${P\; O\; P} = {\log_{10}\frac{( {N_{1} \times N_{2} \times \ldots \mspace{14mu} N_{T}} )^{\frac{1}{T}}}{S}}$

where T is the number of selected terms (nodes) in perspective, Ni isthe number of patents with term “i” (count in node_(i)), and S is thenumber of patents issued in same period of time with all T terms in thespecification. The POP Score is indicative of the level of technicalmaturity and the rate at which potential white spaces are filled. Thesystem computes the dynamic POP score by evaluating the POP Score atdifferent times over a time window of interest. The shape of the curveof the POP score over time illustrates the growth rate of the technicalmaturity. Similar information can be obtained by plotting the derivativeof the POP score against the POP Score (parameterized by time).

Projection techniques may be applied to forecast growth of target nodes(emerging concepts or new nodes). For example, in the illustratedembodiment, the growth rate of node B identified by reference numeral132 may be forecast using a kernel-based regression technique. Thetechnique involves identifying neighborhood nodes (conceptual peers) andcomputing the peers' temporal growth rates over a fixed amount of timein the past. For example, a growth function may be represented asfollows:

-   G(node_(i),t)=g₀(node_(i),t); Kernel is defined as    k[d(node_(i),node_(j))]; distance of neighboring nodes to node of    interest B is represented as d(n_(i), B); contribution values of    each node n_(i), growth rate to growth rate of node B and    represented as k[d(n_(i),B)]. The growth rate of node B is a    combination of node B's historical growth rate and a convex sum of    B's neighbors growth rates and is computed as follows:

${g(B)} = {{\alpha \times {g_{0}(B)}} + {( {1 - \alpha} ) \times \frac{\sum\limits_{i = 1}^{n}{{K\lbrack {d( {{node}_{i},B} )} \rbrack} \times {g_{0}( {node}_{i} )}}}{\sum\limits_{i = 1}^{n}{K\lbrack {d( {{node}_{i},B} )} \rbrack}}}}$

Note that parameter ∝ determines the percentage of node B'sself-contribution (from its own history).

In an alternate embodiment, the projection technique may include anevolutionary-based fuzzy model approach. The process generates acollection of competing models, evaluates their performance in light ofthe currently available data, refines the best models using evolutionarysearch, and selects the best one(s) after a finite number of iterations.This process is typically repeated periodically to automatically produceupdated and improved versions of the model.

Projection techniques may also be applied to links to forecast potentialgrowth of identified white spaces (missing links) by using the growthrates of coupled nodes. For example, the cardinality of a linkrepresented by L (A, B) coupling nodes A and B, is bounded by theminimum of the cardinality of each node and is represented as |L(A,B)|≦Min{|A|,|B|}. The growth rates of nodes A and B would also beconstrained by the same inequality and represented asG(L(A,B))≦Min{G(A),G(B)}.

In an alternate embodiment, the graphical representation may be used forassertion support, in other words detecting possible infringement insupport of licensing efforts. The graph may include several nodes(patents) in a clustered form. One node (reference node) may be locatedat the center and other nodes may be located around the reference node.Assuming a reference patent as the center, the distance from the centerindicates differences of other patents from the reference patent. Thisenables one to analyze individual (similar) patents and drill-down intopatents of interest.

As discussed, the analytical tool in one embodiment is automated orsemi-automated, provides a content rich graphical view and is easy touse. Also, the tool is capable of performing higher-order analysis.Examples of higher-order analysis includes understanding how aparticular competitor is using an optimization technique for innovation,analysis of how company A is compared to company B in terms ofinnovation, for example, in signal processing technologies for acousticsensors; identifying what are the top five percent of patents that formthe backbone of a particular company's research; or the like. Aparticular company can use the exemplary tool to enforce IP regionscovered by the particular company's patents that might be entered byother companies. The tool could also be used as part of due diligences,to analyze strategic IP strengths of potential acquisitions. Also thetool may be used as part of strategic investment analyses to decide IPregions that are blocked and regions available in areas of interest. Theexemplary technique facilitates shortened analysis time, improvedscalability, reduced validation efforts, creating assertionopportunities, and uncovering partnership leverages.

Referring to FIG. 8, a process flow 172 for performing landscapeanalysis using a semi-automated system is disclosed. In the illustratedembodiment, the analysis is related to a hospital optimization domainbut this is merely for illustrative purposes to show a working example.The process includes an initial step of using an appropriate interfacewith a document repository to query and retrieve relevant documents. Forexample, a crawler is used to retrieve documents from Cite seer, MicroPatent, Aureka, or the like. In this example, a query is generatedindicated by 174 in relation to the desired domain. The query mayinclude a plurality of keywords related to the domain, such as hospitaloptimization. The query may include defining a query for retrieval byusing a subset of user-provided keywords (topics, assignees, dates,classification codes, or the like). The query may include definingequivalence classes of keyword synonyms to increase retrieval ofpatents.

The process further includes generating an interactive graphicalrepresentation of neighborhoods of patents connected by similar terms(keywords or labels) indicated by 176. Each node of the graphicalrepresentation is indicative of number of patents containing the term(concept) associated with a particular node. For example, one node maybe associated with healthcare domain, another node may be associatedwith optimization domain, yet another node may be associated withhospital domain, and so forth. Each link of the graphical representationis indicative of number of patents containing the terms (concepts)associated to the nodes that are linked. Term subsumption may be used tomap more specific terms to broader terms that may be used in thegraphical representation. These terms are typically identified andclassified to higher-level terms by text mining a corpus of documentsand using fuzzy clustering techniques.

The process then further includes generating a pie diagram indicated by178, such as the assignees related to the hospital optimization domain.The assignees in the pie diagram may be sorted in particular order(increasing or decreasing order) depending on the number of patentsassigned to each assignee. For example, the pie diagram may includeassignees such as General Electric, Siemens, Hitachi, Nippon Electric,and so forth having patents assigned in the area of hospitaloptimization.

The process further includes generating an assignee heat map indicatedby 180. The assignee heat map may include a number of assignees and therespective number of patents in the associated domain related tohospital optimization. For example, the heat map may indicate the numberof patents assigned to General Electric, Siemens, Hitachi, NipponElectric in the area of patient care. Similarly any number of domainareas and assignees related to hospital optimization are envisaged.

The process also includes visualizing patent clusters (nodes) indicatedby 182 based on patent citations. This may also include identifyingseminal patents indicative of highly connected nodes. A user mayinteractively select and modify a subset of patents, by using any of thecriteria including patents (or seminal patents) in a node, patents (orseminal patents) in a link, patents (or seminal patents) in aneighborhood (a sub portion of the graphical representation). In oneembodiment, seminal patents in nodes may be visualized in a patent view.In another embodiment, seminal patents in neighborhood may be visualizedin a heat map.

The process further includes performing comparative analysis of two ormore assignees using the graphical representation for various domainsindicated by 184, such as related to hospital optimization. For example,green nodes and green links might be indicative that one assignee hasstronger presence in certain areas, for example, optimization,statistics, capacity, bed, or the like and red nodes and red links mightbe indicative that another assignee has stronger presence in theremaining domain areas. In one embodiment, a heat map is used toindicate node cardinality (number of patents) of two or more assigneesor competitors in the various domains related to hospital optimization.It should be noted herein that the process flow illustrated herein is anexemplary embodiment and there is no requirement that the process flowproceed in a particular order. It is reiterated herein that the order ofthe steps may vary depending on the requirement of a user.

While only certain features of the invention have been illustrated anddescribed herein, many modifications and changes will occur to thoseskilled in the art. It is, therefore, to be understood that the appendedclaims are intended to cover all such modifications and changes as fallwithin the true spirit of the invention.

1. A method for analyzing a patent landscape, comprising: inputting aquery to a computing device, said computing device coupled to at leastone database; retrieving a plurality of relevant patents indicative of aconceptual region of the patent landscape from a patent repository onsaid database based on the query; generating one or more presentationsbased on the retrieved relevant patents; conducting analysis of theplurality of relevant patents based on the one or more presentationsusing an interactive visualization technique via the computing device;wherein the interactive visualization technique comprises generating aninteractive graphical representation of a plurality of nodesinter-coupled through links; wherein each node is indicative of a numberof patents of the patent landscape comprising at least one termrepresentative of the node; wherein each link is indicative of a numberof patents of the patent landscape comprising one or more termsrepresentative of the nodes coupled via the link; displaying an outputbased on the competitive analysis via an output device.
 2. The method ofclaim 1, wherein conducting analysis comprises analyzing cross companylandscape, relative strengths and weaknesses, semantically adjacentdomains, or combinations thereof of the plurality of relevant patents.3. The method of claim 1, wherein node diameter of a node is indicativeof number of patents with reference to a domain associated with thenode.
 4. The method of claim 1, wherein node color is indicative of acharacteristic associated with a predetermined competitor company. 5.The method of claim 1, wherein link color is indicative of acharacteristic associated with a predetermined competitor company. 6.The method of claim 1, wherein proximity of one node with one or moreother nodes is indicative of conceptual similarity in the patentlandscape.
 7. The method of claim 1, further comprising performinginteractive visualization and navigation of the interactive graphicalrepresentation via rotations and zooming operations.
 8. The method ofclaim 7, further comprising switching visualization modes to limit thescope of the invention; wherein visualization modes comprises inventionscope, invention status, assignee, node cardinality, link cardinality,filed date, issue date, patent quality, or combinations thereof.
 9. Themethod of claim 8, wherein invention scope comprises title, abstract,claim, or entire invention of a patent or a patent application.
 10. Themethod of claim 8, wherein invention status comprises at least one ofactive issued patents, expired issued patents, foreign patents, andpublished patent applications.
 11. The method of claim 1, furthercomprising analyzing relative strengths and weaknesses of one or moreassignees based on a general view, competitive view, focused view,focused competitive view, or combinations thereof of nodes and links.12. The method of claim 11, wherein analyzing general view comprisesanalyzing node size, and link thickness indicative of the general viewof a set of assignees or a single assignee.
 13. The method of claim 11,wherein analyzing competitive view comprises include selecting referenceassignee from a set of assignees, node and link color representative ofassignee with larger patent count for selected terms, and changingreference assignee within a set of assignees to analyze localintellectual property dominance.
 14. The method of claim 11, whereinanalyzing focused view comprises focusing on a node of interest.
 15. Themethod of claim 11, wherein analyzing focused competitive view comprisesevaluating coverage of a set of assignees.
 16. The method of claim 2,wherein analyzing semantically adjacent domains comprises selectingspecific nodes of interest.
 17. The method of claim 1, furthercomprising analyzing temporal trends based on the interactive graphicalrepresentation.
 18. The method of claim 1, further comprisingidentifying seminal patents categorized by conceptual area based on theinteractive graphical representation.
 19. The method of claim 1, furthercomprising identifying predetermined nodes to compute peer temporaltrends.
 20. The method of claim 19, wherein computing peer temporaltrends comprises analyzing size of nodes and links.
 21. A semi-automatedmethod for interactively analyzing a patent landscape, comprising:inputting a query to a computing device via an input device, wherein theinput device is coupled to one or more databases; retrieving a pluralityof relevant documents indicative of a conceptual region of the patentlandscape from a data warehouse based on the query; generating one ormore presentations based on the retrieved relevant documents via thecomputing device; augmenting the one or more presentations using one ormore reasoning techniques via the computing device; conductingcompetitive analysis of the plurality of relevant patents based on theone or more presentations, using an interactive visualization techniquevia the computing device; wherein the interactive visualizationtechnique comprises generating an interactive network based graphicalrepresentation of a plurality of nodes inter-coupled through links;wherein each node is indicative of number of patents of the patentlandscape comprising a term representative of the node; wherein eachlink is indicative of number of patents of the patent landscapecomprising one or more terms representative of the nodes coupled via thelink; and displaying an output based on the competitive analysis via anoutput device; wherein the competitive analysis output is used forintellectual property enforcement, due diligence, and strategicinvestment analysis.
 22. The method of claim 21; wherein one or morereasoning techniques comprises lexical reasoning, taxonomical reasoning,deductive reasoning, analogical reasoning, temporal reasoning, orcombinations thereof.
 23. The method of claim 21, wherein lexicalreasoning comprises obtaining a set of keywords by automated keywordextraction and merging results of key word extraction.
 24. The method ofclaim 21, wherein taxonomical reasoning comprises augmenting lexicalinformation by using domain ontologies.
 25. The method of claim 21,wherein deductive reasoning comprises augmenting lexical information byusing predetermined domain rules.
 26. The method of claim 21, whereinanalogical reasoning comprises use of relationships established in onedomain and transferring to another domain.
 27. The method of claim 21,further comprising evaluating white space opportunities based on the oneor more presentations; wherein evaluating white space opportunitiescomprises identifying pairs of nodes that are not directly coupled toeach other.
 28. The method of claim 27, further comprising listing andranking the white space opportunities based on nodes that are to bedirectly coupled to each other.
 29. The method of claim 28, furthercomprising applying projection techniques to forecast growth ofpredetermined target nodes.
 30. The method of claim 29, furthercomprising applying projection techniques to links to forecast potentialgrowth of identified white space opportunities.
 31. The method of claim30, further comprising computing a dynamic prescience of patent scoreindicative of the level of technical maturity and the rate at whichpotential white spaces are filled.
 32. A semi-automated system having atleast one computing device for interactively analyzing a patentlandscape, comprising: an input device configured to input a query to apatent database; an interactive analytical tool communicatively coupledto the patent database, wherein said tool is configured to extract aplurality of relevant patents indicative of a predetermined conceptualregion of the patent landscape; generate one or more presentations basedon the retrieved relevant patents; conduct competitive analysis of theplurality of relevant patents based on the one or more presentationsusing an interactive visualization technique; augment the one or morepresentations based one or more reasoning techniques; evaluate whitespace opportunities based on the one or more presentations; wherein theinteractive visualization technique comprises generating an interactivenetwork based graphical representation of a plurality of nodesinter-coupled through links; wherein each node is indicative of numberof patents of the patent landscape comprising a term representative ofthe node; wherein each link is indicative of number of patents of thepatent landscape comprising one or more terms representative of thenodes coupled via the link; an output device configured to display anoutput indicative of an analysis output from the analytical tool;wherein the output is used for intellectual property enforcement, duediligence, and strategic investment analysis.